“Die Geschichte unseres Landes ist so viel mehr als nur der Zweite Weltkrieg”: German Cultural History Meets Transnational Genre Narratives in <i>How to Sell Drugs Online (Fast)</i>
Bibliographic record
Abstract
This article examines Netflix’s third original German series, How to Sell Drugs Online (Fast) (2019–), in order to situate German (streaming) television in the broader media developments of the convergence era. Transnationally conceived from the outset, the series combines transnational genre narratives with German cultural history in order to address broad audiences across the globe. Focusing on narrative complexity, intertextuality, and genre hybridity, the article first analyzes the series’ references to both national and international nerd narratives such as Baran bo Odar’s Who am I: Kein System ist sicher (2014) and David Fincher’s The Social Network (2010) . Then it contextualizes How to Sell Drugs within twenty-first-century societal debates in Germany and canonical works from nineteenth-century German literary tradition—especially Johann Wolfgang Goethe’s Die Leiden des jungen Werthers (1774) and Faust (1808) , as well as Frank Wedekind’s Frühlings Erwachen (1891) . In doing so, this article demonstrates that the series creates ambiguous images simultaneously inspired by both German and Anglo-American cultural history to appeal to audiences around the globe.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".